↳ Machine+learning

The uses of algorithms discussed in the first part of this article vary widely: from hiring decisions to bail assignment, to political campaigns and military intelligence.

Across all these applications of machine learning methods, there is a common thread: Data on individuals is used to treat different individuals differently. In the past, broadly speaking, such commercial and government activities used to target everyone in a given population more or less similarly—the same advertisements, the same prices, the same political slogans. More and more now, everyone gets personalized advertisements, personalized prices, and personalized political messages. New inequalities are created and new fragmentations of discourse are introduced.

Is that a problem? Well, it depends. I will discuss two types of concerns. The first type, relevant in particular to news and political messaging, is that the differentiation of messages is by itself a source of problems.

Terminology like "machine learning," "artificial intelligence," "deep learning," and "neural nets" is pervasive: business, universities, intelligence agencies, and political parties are all anxious to maintain an edge over the use of these technologies. Statisticians might be forgiven for thinking that this hype simply reflects the success of the marketing speak of Silicon Valley entrepreneurs vying for venture capital. All these fancy new terms are just describing something statisticians have been doing for at least two centuries.

But recent years have indeed seen impressive new achievements for various prediction problems, which are finding applications in ever more consequential aspects of society: advertising, incarceration, insurance, and war are all increasingly defined by the capacity for statistical prediction. And there is crucial a thread that ties these widely disparate applications of machine learning together: the use of data on individuals to treat different individuals differently. In this two part post, Max Kasy surveys the politics of the machine learning landscape.